Effective Attention Modeling for Neural Relation Extraction
This work addresses a specific problem in natural language processing for researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the challenge of relation extraction in long sentences with distant entities by proposing a novel attention model that incorporates syntactic information and multi-factor attention, achieving state-of-the-art performance on the New York Times corpus.
Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.